A Stochastic Biofilm Disruption Model based on Quorum Sensing Mimickers

TL;DR

This paper introduces a stochastic model for biofilm disruption using quorum sensing mimickers, validated with experimental data, revealing multiple thresholds for effective biofilm breakdown.

Contribution

It presents a novel stochastic CRN-based model for biofilm disruption via QS mimickers, including a new simulation algorithm and multiple disruption thresholds.

Findings

Model captures uncertainty in biofilm state transitions.

Identifies three thresholds for QS activation, EPS disruption, and bacterial disruption.

Validated with in vitro Pseudomonas aeruginosa biofilm experiments.

Abstract

Quorum sensing (QS) mimickers can be used as an effective tool to disrupt biofilms which consist of communicating bacteria and extracellular polymeric substances. In this paper, a stochastic biofilm disruption model based on the usage of QS mimickers is proposed. A chemical reaction network (CRN) involving four different states is employed to model the biological processes during the biofilm formation and its disruption via QS mimickers. In addition, a state-based stochastic simulation algorithm is proposed to simulate this CRN. The proposed model is validated by the in vitro experimental results of Pseudomonas aeruginosa biofilm and its disruption by rosmarinic acid as the QS mimicker. Our results show that there is an uncertainty in state transitions due to the effect of the randomness in the CRN. In addition to the QS activation threshold, the presented work demonstrates that there are underlying two more thresholds for the disruption of EPS and bacteria, which provides a realistic modeling for biofilm disruption with QS mimickers.